the inefficent loop
This loop runs through the netcdf files and then looks for which rows in data_aea it should extract the value to point from, and at what depth (netcDF layer)
Start with 2007_10 data
strt <- Sys.time() #get the start time
xy <- testbk190000[ ,c("longitude_","latitude_m")] # This is to tell R where the coordinates are. Note that the column order needs to be longitude, latitude
testbk190000sp <- SpatialPointsDataFrame(coords = xy, data = testbk190000, proj4string = CRS("+proj=aea +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-60 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")) # The CRS is used here is for the albers equal area projection.
netcdf_list <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = TRUE) #true means the full path is included
no_netcdf <- length(netcdf_list) #for the loop - need to know how many files to cycle through
netcdf_name <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = FALSE) #false means the path is not included
aea <- raster("../output/env/aea.tif")
yr <- 2007 # a variable for the observation year
mth <- 10 # a variable for the observation month
for (i in 1:no_netcdf) {
print(netcdf_name[i]) #this just prints the name of the netCDF R is working one
brkyr <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 1)) # extracting the first part of the netcdf filename (which is the year)
brkmth <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 2)) # extracting the second part of the netcdf filename (which is the month)
brkvar <- (sapply(strsplit(netcdf_name[i], "_"), "[[", 3)) # extracting the third part of the netcdf (inc.nc)
temp_brick <- brick(netcdf_list[i], lvar = 4)
temp_brick <- projectRaster(temp_brick, aea)
for (j in 1:nrow(testbk190000sp)) {
de <- testbk190000sp$depthlayerno[[j]] # a variable for the observation depth layer
if (brkyr == yr & brkmth == mth & brkvar == "temp.nc"){
testbk190000sp$temp_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ])
if (is.na(de)){
testbk190000sp$temp_depth[j] <- NA
} else
testbk190000sp$temp_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "salinity.nc") {
testbk190000sp$salinity_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ])
if (is.na(de)){
testbk190000sp$salinity_depth[j] <- NA
} else
testbk190000sp$salinity_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "chl.nc") {
testbk190000sp$chl_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ])
if (is.na(de)){
testbk190000sp$chl_depth[j] <- NA
} else
testbk190000sp$chl_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "o2.nc") {
testbk190000sp$o2_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ])
if (is.na(de)){
testbk190000sp$o2_depth[j] <- NA
} else
testbk190000sp$o2_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "mlp.nc") {
testbk190000sp$mlp_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "ssh.nc") {
testbk190000sp$ssh_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ])
}
}
}
[1] "2007_10_chl.nc"
ok now create the first lot of random for 2007_10
testbk100001999 <- test_back_df[sample(nrow(test_back_df), 10000), ] #where 10000 = number of rows to sample (large sample as per maxent)
testbk200001999 <- test_back_df[sample(nrow(test_back_df), 20000), ]
testbk300001999 <- test_back_df[sample(nrow(test_back_df), 30000), ]
testbk500001999 <- test_back_df[sample(nrow(test_back_df), 50000), ]
testbk1000001999 <- test_back_df[sample(nrow(test_back_df), 100000), ]
testbk1900001999 <- test_back_df[sample(nrow(test_back_df), 190000), ]
plot each variable against the different no of background points
ggplot(test10000, aes(x = ssh_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/ssh_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = mlp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/mlp_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = temp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/temp_surface_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = temp_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/temp_depth_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = salinity_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/salinity_surface_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = salinity_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/salinity_depth_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = chl_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/chl_surface_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = chl_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/chl_depth_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = o2_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/o2_surface_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

ggplot(test10000, aes(x = o2_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../data/o2_depth_back_no.png") # to automatically save the plot to a png AND show it inline
png
3
dev.off() # stops automatic saving of the plot to a png
png
2

Try again for another month - say 1999 02 AND again 2014 06
This time extract the values for all points and then subset
strt <- Sys.time() #get the start time
xy <- testbglist[ ,c("longitude_","latitude_m")] # This is to tell R where the coordinates are. Note that the column order needs to be longitude, latitude
testbglistsp <- SpatialPointsDataFrame(coords = xy, data = testbglist, proj4string = CRS("+proj=aea +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-60 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")) # The CRS is used here is for the albers equal area projection.
netcdf_list <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = TRUE) #true means the full path is included
no_netcdf <- length(netcdf_list) #for the loop - need to know how many files to cycle through
netcdf_name <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = FALSE) #false means the path is not included
aea <- raster("../output/env/aea.tif")
yr <- 1999 # a variable for the observation year
mth <- 02 # a variable for the observation month
for (i in 1:no_netcdf) {
print(netcdf_name[i]) #this just prints the name of the netCDF R is working one
brkyr <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 1)) # extracting the first part of the netcdf filename (which is the year)
brkmth <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 2)) # extracting the second part of the netcdf filename (which is the month)
brkvar <- (sapply(strsplit(netcdf_name[i], "_"), "[[", 3)) # extracting the third part of the netcdf (inc.nc)
temp_brick <- brick(netcdf_list[i], lvar = 4)
temp_brick <- projectRaster(temp_brick, aea)
for (j in 1:nrow(testbglistsp)) {
de <- testbglistsp$depthlayerno[[j]] # a variable for the observation depth layer
if (brkyr == yr & brkmth == mth & brkvar == "temp.nc"){
testbglistsp$temp_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
if (is.na(de)){
testbglistsp$temp_depth[j] <- NA
} else
testbglistsp$temp_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "salinity.nc") {
testbglistsp$salinity_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
if (is.na(de)){
testbglistsp$salinity_depth[j] <- NA
} else
testbglistsp$salinity_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "chl.nc") {
testbglistsp$chl_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
if (is.na(de)){
testbglistsp$chl_depth[j] <- NA
} else
testbglistsp$chl_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "o2.nc") {
testbglistsp$o2_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
if (is.na(de)){
testbglistsp$o2_depth[j] <- NA
} else
testbglistsp$o2_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "mlp.nc") {
testbglistsp$mlp_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
} else if (brkyr == yr & brkmth == mth & brkvar == "ssh.nc") {
testbglistsp$ssh_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
}
}
}
[1] "1999_02_chl.nc"
[1] "1999_02_mlp.nc"
no non-missing arguments to min; returning Infno non-missing arguments to max; returning -Inf
[1] "1999_02_o2.nc"
[1] "1999_02_salinity.nc"
[1] "1999_02_ssh.nc"
no non-missing arguments to min; returning Infno non-missing arguments to max; returning -Inf
[1] "1999_02_temp.nc"
write.csv(testbglistsp, "../data/env/background_point_check/1999_02/testbglistoutput.csv", row.names = FALSE)
#test_back_df <- as.data.frame(testbk100000sp)
#head(test_back_df)
print(Sys.time()-strt) #time it took to run
Time difference of 3.277501 hours
ok now create the first lot of random for 1999_02
testbk100001999 <- test_back_df[sample(nrow(test_back_df), 10000), ] #where 10000 = number of rows to sample (large sample as per maxent)
testbk200001999 <- test_back_df[sample(nrow(test_back_df), 20000), ]
testbk300001999 <- test_back_df[sample(nrow(test_back_df), 30000), ]
testbk500001999 <- test_back_df[sample(nrow(test_back_df), 50000), ]
testbk1000001999 <- test_back_df[sample(nrow(test_back_df), 100000), ]
testbk1900001999 <- test_back_df[sample(nrow(test_back_df), 190000), ]
and plot
ggplot(testbk100001999, aes(x = ssh_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_ssh_back.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = mlp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_mlp.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = temp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_temp_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = temp_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_temp_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = salinity_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_salinity_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = salinity_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_salinity_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = chl_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_chl_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = chl_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_chl_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = o2_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500001999 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_o2_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100001999, aes(x = o2_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500001999 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_o2_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
And now 2014_06
#head(test_back_df)
print(Sys.time()-strt) #time it took to run
Time difference of 3.455486 hours
ok now create the first lot of random for 2014_06
testbk100002014 <- test_back_df[sample(nrow(test_back_df), 10000), ] #where 10000 = number of rows to sample (large sample as per maxent)
testbk200002014 <- test_back_df[sample(nrow(test_back_df), 20000), ]
testbk300002014 <- test_back_df[sample(nrow(test_back_df), 30000), ]
testbk500002014 <- test_back_df[sample(nrow(test_back_df), 50000), ]
testbk1000002014 <- test_back_df[sample(nrow(test_back_df), 100000), ]
testbk1900002014 <- test_back_df[sample(nrow(test_back_df), 190000), ]
and plot
ggplot(testbk100002014, aes(x = ssh_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_ssh.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = mlp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_mlp.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = temp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_temp_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = temp_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_temp_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = salinity_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_salinity_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = salinity_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_salinity_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = chl_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_chl_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = chl_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_chl_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = o2_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_o2_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
ggplot(testbk100002014, aes(x = o2_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_o2_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
---
title: "background points - is more better?"
author: "Samantha Andrews"
output: 
  html_notebook: 
editor_options: 
  chunk_output_type: inline
---

# Overview
quick test to see what happens to distribution of env. variables if add more background points (is 10k enough or do I need more)

A note to anyone who might happen to stumble across this... I am a beginner in R and have had no exposure to similar languages. I don't know what I'm doing. The code herein is unlikely to be elegant and there are probably more efficient ways of running the code.

Built with 'r getRversion()'.

# Package dependencies
You can load them using the following code which uses a function called [ipak](https://gist.github.com/stevenworthington/3178163). 
Note this function checks to see if the packages are installed first.
The "include=FALSE" supresses the package installation text appearing in the document...
```{r pre-install packages, include=FALSE}
packages <- c("ncdf4", "raster", "ggplot2", "plotly") 
source("../src/ipak.R")
ipak(packages)
```

load the raw background.csv file with all points to randomly extract points from (../output/env/unique_cell_centroid_lonlat_nafo2_depth.csv)

```{r}
testbglist <- read.csv("../output/env/unique_cell_centroid_lonlat_nafo2_depth.csv", header = TRUE)
head(testbglist)
```

```{r}
backobsno <- nrow(testbglist)
backobsno
```



# the inefficent loop



This loop runs through the netcdf files and then looks for which rows in data_aea it should extract the value to point from, and at what depth (netcDF layer)

Start with 2007_10 data

```{r}
strt <- Sys.time() #get the start time

xy <- testbglist[ ,c("longitude_","latitude_m")] # This is to tell R where the coordinates are. Note that the column order needs to be longitude, latitude
testbglistsp <- SpatialPointsDataFrame(coords = xy, data = testbglist, proj4string = CRS("+proj=aea +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-60 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")) # The CRS is used here is for the albers equal area projection.


netcdf_list <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = TRUE) #true means the full path is included
no_netcdf <- length(netcdf_list) #for the loop - need to know how many files to cycle through
netcdf_name <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = FALSE) #false means the path is not included
aea <- raster("../output/env/aea.tif") 
yr <- 2007  # a variable for the observation year
mth <- 10  # a variable for the observation month

for (i in 1:no_netcdf) {  
  print(netcdf_name[i]) #this just prints the name of the netCDF R is working one
  brkyr <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 1)) # extracting the first part of the netcdf filename (which is the year)
  brkmth <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 2)) # extracting the second part of the netcdf filename (which is the month)
  brkvar <- (sapply(strsplit(netcdf_name[i], "_"), "[[", 3)) # extracting the third part of the netcdf (inc.nc)
  temp_brick <- brick(netcdf_list[i], lvar = 4)
  temp_brick <- projectRaster(temp_brick, aea) 
    for (j in 1:nrow(testbk190000sp)) {  
      de <- testbk190000sp$depthlayerno[[j]]  # a variable for the observation depth layer
          if (brkyr == yr & brkmth == mth & brkvar == "temp.nc"){
              testbk190000sp$temp_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ]) 
              if (is.na(de)){
                testbk190000sp$temp_depth[j] <- NA
              } else  
                testbk190000sp$temp_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ])
          } else if (brkyr == yr & brkmth == mth & brkvar == "salinity.nc") {
              testbk190000sp$salinity_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ]) 
              if (is.na(de)){
                testbk190000sp$salinity_depth[j] <- NA
              } else  
                testbk190000sp$salinity_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "chl.nc") {
              testbk190000sp$chl_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ]) 
              if (is.na(de)){
                testbk190000sp$chl_depth[j] <- NA
              } else  
                testbk190000sp$chl_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "o2.nc") {
              testbk190000sp$o2_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ]) 
              if (is.na(de)){
                testbk190000sp$o2_depth[j] <- NA
              } else  
                testbk190000sp$o2_depth[j] <- extract(x=temp_brick[[de]], y = testbk190000sp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "mlp.nc") {
              testbk190000sp$mlp_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ])
          } else if (brkyr == yr & brkmth == mth & brkvar == "ssh.nc") {
              testbk190000sp$ssh_surface[j] <- extract(x=temp_brick[[1]], y = testbk190000sp[j, ]) 
            
          }
     
    }
}
write.csv(testbglistsp, "../data/env/background_point_check/200710_allbackgroundpoints.csv", row.names = FALSE)
test_back_df <- as.data.frame(testbglistsp)
print(Sys.time()-strt) #time it took to run
```

ok now create the first lot of random for 2007_10

```{r}
testbk100001999 <- test_back_df[sample(nrow(test_back_df), 10000), ]  #where 10000 = number of rows to sample (large sample as per maxent)
testbk200001999 <- test_back_df[sample(nrow(test_back_df), 20000), ]  
testbk300001999 <- test_back_df[sample(nrow(test_back_df), 30000), ]  
testbk500001999 <- test_back_df[sample(nrow(test_back_df), 50000), ]  
testbk1000001999 <- test_back_df[sample(nrow(test_back_df), 100000), ]  
testbk1900001999 <- test_back_df[sample(nrow(test_back_df), 190000), ]  
```



plot each variable against the different no of background points



```{r}
ggplot(test10000, aes(x = ssh_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_ssh_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = mlp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_mlp_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = temp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_temp_surface_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = temp_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_temp_depth_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = salinity_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_salinity_surface_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = salinity_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_salinity_depth_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = chl_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_chl_surface_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = chl_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_chl_depth_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = o2_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_o2_surface_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(test10000, aes(x = o2_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=test20000 , na.rm = TRUE, colour = "blue") + geom_density(data=test50000 , na.rm = TRUE, colour = "green") + geom_density(data=test100000 , na.rm = TRUE, colour = "orange") + geom_density(data=test190000 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/200710_o2_depth_back_no.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
```

Try again for another month - say 1999 02 AND again 2014 06

This time extract the values for all points and then subset

```{r}
strt <- Sys.time() #get the start time

xy <- testbglist[ ,c("longitude_","latitude_m")] # This is to tell R where the coordinates are. Note that the column order needs to be longitude, latitude
testbglistsp <- SpatialPointsDataFrame(coords = xy, data = testbglist, proj4string = CRS("+proj=aea +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-60 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")) # The CRS is used here is for the albers equal area projection.


netcdf_list <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = TRUE) #true means the full path is included
no_netcdf <- length(netcdf_list) #for the loop - need to know how many files to cycle through
netcdf_name <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = FALSE) #false means the path is not included
aea <- raster("../output/env/aea.tif") 
yr <- 1999  # a variable for the observation year
mth <- 02  # a variable for the observation month

for (i in 1:no_netcdf) {  
  print(netcdf_name[i]) #this just prints the name of the netCDF R is working one
  brkyr <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 1)) # extracting the first part of the netcdf filename (which is the year)
  brkmth <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 2)) # extracting the second part of the netcdf filename (which is the month)
  brkvar <- (sapply(strsplit(netcdf_name[i], "_"), "[[", 3)) # extracting the third part of the netcdf (inc.nc)
  temp_brick <- brick(netcdf_list[i], lvar = 4)
  temp_brick <- projectRaster(temp_brick, aea) 
    for (j in 1:nrow(testbglistsp)) {  
      de <- testbglistsp$depthlayerno[[j]]  # a variable for the observation depth layer
          if (brkyr == yr & brkmth == mth & brkvar == "temp.nc"){
              testbglistsp$temp_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$temp_depth[j] <- NA
              } else  
                testbglistsp$temp_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ])
          } else if (brkyr == yr & brkmth == mth & brkvar == "salinity.nc") {
              testbglistsp$salinity_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$salinity_depth[j] <- NA
              } else  
                testbglistsp$salinity_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "chl.nc") {
              testbglistsp$chl_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$chl_depth[j] <- NA
              } else  
                testbglistsp$chl_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "o2.nc") {
              testbglistsp$o2_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$o2_depth[j] <- NA
              } else  
                testbglistsp$o2_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "mlp.nc") {
              testbglistsp$mlp_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
          } else if (brkyr == yr & brkmth == mth & brkvar == "ssh.nc") {
              testbglistsp$ssh_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
            
          }
     
    }
}
write.csv(testbglistsp, "../data/env/background_point_check/199902_allbackgroundpoints.csv", row.names = FALSE)
test_back_df <- as.data.frame(testbglistsp)
print(Sys.time()-strt) #time it took to run
```

ok now create the first lot of random for 1999_02

```{r}
testbk100001999 <- test_back_df[sample(nrow(test_back_df), 10000), ]  #where 10000 = number of rows to sample (large sample as per maxent)
testbk200001999 <- test_back_df[sample(nrow(test_back_df), 20000), ]  
testbk300001999 <- test_back_df[sample(nrow(test_back_df), 30000), ]  
testbk500001999 <- test_back_df[sample(nrow(test_back_df), 50000), ]  
testbk1000001999 <- test_back_df[sample(nrow(test_back_df), 100000), ]  
testbk1900001999 <- test_back_df[sample(nrow(test_back_df), 190000), ]  
```

and plot


```{r}
ggplot(testbk100001999, aes(x = ssh_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_ssh_back.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = mlp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_mlp.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = temp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_temp_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = temp_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_temp_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = salinity_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_salinity_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = salinity_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_salinity_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = chl_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_chl_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = chl_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_chl_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = o2_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500001999 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_o2_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100001999, aes(x = o2_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200001999 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500001999 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000001999 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900001999 , na.rm = TRUE, colour = "pink")
dev.copy(png,".../output/env/background_point_check/199902_o2_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
```


And now 2014_06


```{r}
strt <- Sys.time() #get the start time

xy <- testbglist[ ,c("longitude_","latitude_m")] # This is to tell R where the coordinates are. Note that the column order needs to be longitude, latitude
testbglistsp <- SpatialPointsDataFrame(coords = xy, data = testbglist, proj4string = CRS("+proj=aea +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-60 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")) # The CRS is used here is for the albers equal area projection.


netcdf_list <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = TRUE) #true means the full path is included
no_netcdf <- length(netcdf_list) #for the loop - need to know how many files to cycle through
netcdf_name <- list.files("../data/bktstncdf", pattern = '*.nc', full.names = FALSE) #false means the path is not included
aea <- raster("../output/env/aea.tif") 
yr <- 2014  # a variable for the observation year
mth <- 06  # a variable for the observation month

for (i in 1:no_netcdf) {  
  print(netcdf_name[i]) #this just prints the name of the netCDF R is working one
  brkyr <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 1)) # extracting the first part of the netcdf filename (which is the year)
  brkmth <- as.integer(sapply(strsplit(netcdf_name[i], "_"), "[[", 2)) # extracting the second part of the netcdf filename (which is the month)
  brkvar <- (sapply(strsplit(netcdf_name[i], "_"), "[[", 3)) # extracting the third part of the netcdf (inc.nc)
  temp_brick <- brick(netcdf_list[i], lvar = 4)
  temp_brick <- projectRaster(temp_brick, aea) 
    for (j in 1:nrow(testbglistsp)) {  
      de <- testbglistsp$depthlayerno[[j]]  # a variable for the observation depth layer
          if (brkyr == yr & brkmth == mth & brkvar == "temp.nc"){
              testbglistsp$temp_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$temp_depth[j] <- NA
              } else  
                testbglistsp$temp_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ])
          } else if (brkyr == yr & brkmth == mth & brkvar == "salinity.nc") {
              testbglistsp$salinity_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$salinity_depth[j] <- NA
              } else  
                testbglistsp$salinity_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "chl.nc") {
              testbglistsp$chl_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$chl_depth[j] <- NA
              } else  
                testbglistsp$chl_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "o2.nc") {
              testbglistsp$o2_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
              if (is.na(de)){
                testbglistsp$o2_depth[j] <- NA
              } else  
                testbglistsp$o2_depth[j] <- extract(x=temp_brick[[de]], y = testbglistsp[j, ]) 
          } else if (brkyr == yr & brkmth == mth & brkvar == "mlp.nc") {
              testbglistsp$mlp_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ])
          } else if (brkyr == yr & brkmth == mth & brkvar == "ssh.nc") {
              testbglistsp$ssh_surface[j] <- extract(x=temp_brick[[1]], y = testbglistsp[j, ]) 
            
          }
     
    }
}
write.csv(testbglistsp, "../data/env/background_point_check/201406_allbackgroundpoints.csv", row.names = FALSE)
test_back_df <- as.data.frame(testbglistsp)
print(Sys.time()-strt) #time it took to run
```
ok now create the first lot of random for 2014_06

```{r}
testbk100002014 <- test_back_df[sample(nrow(test_back_df), 10000), ]  #where 10000 = number of rows to sample (large sample as per maxent)
testbk200002014 <- test_back_df[sample(nrow(test_back_df), 20000), ]  
testbk300002014 <- test_back_df[sample(nrow(test_back_df), 30000), ]  
testbk500002014 <- test_back_df[sample(nrow(test_back_df), 50000), ]  
testbk1000002014 <- test_back_df[sample(nrow(test_back_df), 100000), ]  
testbk1900002014 <- test_back_df[sample(nrow(test_back_df), 190000), ]  
```

and plot


```{r}
ggplot(testbk100002014, aes(x = ssh_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_ssh.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = mlp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_mlp.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = temp_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_temp_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = temp_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_temp_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = salinity_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_salinity_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = salinity_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_salinity_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = chl_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_chl_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = chl_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_chl_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = o2_surface)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_o2_surface.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

ggplot(testbk100002014, aes(x = o2_depth)) + geom_density(na.rm = TRUE, colour = "red") + geom_density(data=testbk200002014 , na.rm = TRUE, colour = "blue") + geom_density(data=testbk500002014 , na.rm = TRUE, colour = "green") + geom_density(data=testbk1000002014 , na.rm = TRUE, colour = "orange") + geom_density(data=testbk1900002014 , na.rm = TRUE, colour = "pink")
dev.copy(png,"../output/env/background_point_check/201406_o2_depth.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png
```



# 3d plot background points

```{r}
bck2014_06_3d <- plot_ly(x= testbk100002014$longitude_, y = testbk100002014$latitude_m, z = testbk100002014$depthlayerno)
bck2014_06_3d
```

# 2d plot background points

```{r}
bck2014_06_2d <- plot(x= testbk100002014$longitude_, y = testbk100002014$latitude_m)
dev.copy(png,"../data/env/background_point_check/bck10000_2d.png") # to automatically save the plot to a png AND show it inline
dev.off() # stops automatic saving of the plot to a png

```